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Manuel Baum

Research interests

A large part of the robotics community develops
specialized tools to solve specific problems. If we want to compose
these tools into a larger system that behaves puroposefully, then we
need to understand how these methods can interact with each other. I
want to find out how we can organize such processes that generate
behavior.

The domain in which I examine this is robotic
manipulation. Right now, I am interested in the relation between
motion planning and motion control. These are problems and methods on
different scales of time and space, yet time and space are continuous,
so a hard cut between such methods may be deficient.

I
believe that a system which generates behavior should reflect the
discreteness and continuousness of its problem setting. The analysis
and decomposition of problem structure can naturally lead to a
suitable decomposition of a system that acts to solve such a problem.
I try to understand how such decomposition can be done
automatically.

A first step towards this was my master's
thesis where I worked with MMC, a kind of neural network that
decomposes the computation of robot kinematics into the simultaneous
solution of multiple smaller problems.

I am also interested
in pattern formation, procedural geometry, drawing and
painting.

Publications

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Manuel Baum and Martin Meier and Malte Schilling
2015. Population based Mean of Multiple Computations networks: A
building block for kinematic models [6]. 2015 International Joint
Conference on Neural Networks (IJCNN), 1–8..

Manuel Baum and Matthew Bernstein and Roberto
Martín-Martín and Sebastian Höfer and Johannes Kulick and Marc
Toussaint and Alex Kacelnik and Oliver Brock 2017. Opening a Lockbox
through Physical Exploration [7]. Proceedings of the IEEE
International Conference on Humanoid Robots (Humanoids).